





'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   lowerCamelCase__    							=      (PNDMScheduler,)
   lowerCamelCase__    							=      (('''num_inference_steps''', 50),)

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[str]    =  {
        """num_train_timesteps""": 1_0_0_0,
        """beta_start""": 0.0001,
        """beta_end""": 0.02,
        """beta_schedule""": """linear""",
    }

    config.update(**__SCREAMING_SNAKE_CASE       )
    return config

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=0	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[str]    =  dict(self.forward_default_kwargs       )
    snake_case__     :					Union[str, Any]    =  kwargs.pop("""num_inference_steps"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[str]    =  self.dummy_sample
    snake_case__     :					Any    =  0.1 * sample
    snake_case__     :					Tuple    =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

    for scheduler_class in self.scheduler_classes:
     snake_case__     :					Dict    =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Optional[int]    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )
     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
     # copy over dummy past residuals
     snake_case__     :					str    =  dummy_past_residuals[:]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Dict    =  scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )
      new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
      # copy over dummy past residuals
      snake_case__     :					Any    =  dummy_past_residuals[:]

     snake_case__     :					int    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					str    =  new_scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

     snake_case__     :					Tuple    =  scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					Optional[Any]    =  new_scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

   def 		__UpperCamelCase						(							self       ):
    pass

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=0	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[Any]    =  dict(self.forward_default_kwargs       )
    snake_case__     :					str    =  kwargs.pop("""num_inference_steps"""	,		__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  self.dummy_sample
    snake_case__     :					Optional[Any]    =  0.1 * sample
    snake_case__     :					int    =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

    for scheduler_class in self.scheduler_classes:
     snake_case__     :					List[Any]    =  self.get_scheduler_config()
     snake_case__     :					str    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )
     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

     # copy over dummy past residuals (must be after setting timesteps)
     snake_case__     :					Dict    =  dummy_past_residuals[:]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Union[str, Any]    =  scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )
      # copy over dummy past residuals
      new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

      # copy over dummy past residual (must be after setting timesteps)
      snake_case__     :					List[str]    =  dummy_past_residuals[:]

     snake_case__     :					int    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					Dict    =  new_scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

     snake_case__     :					int    =  scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					int    =  new_scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					Any    =  self.scheduler_classes[0]
    snake_case__     :					int    =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Dict    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  1_0
    snake_case__     :					int    =  self.dummy_model()
    snake_case__     :					Any    =  self.dummy_sample_deter
    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

    for i, t in enumerate(scheduler.prk_timesteps       ):
     snake_case__     :					Dict    =  model(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
     snake_case__     :					List[Any]    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).prev_sample

    for i, t in enumerate(scheduler.plms_timesteps       ):
     snake_case__     :					str    =  model(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
     snake_case__     :					List[Any]    =  scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).prev_sample

    return sample

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  dict(self.forward_default_kwargs       )

    snake_case__     :					str    =  kwargs.pop("""num_inference_steps"""	,		__SCREAMING_SNAKE_CASE       )

    for scheduler_class in self.scheduler_classes:
     snake_case__     :					Optional[Any]    =  self.get_scheduler_config()
     snake_case__     :					str    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

     snake_case__     :					Any    =  self.dummy_sample
     snake_case__     :					Tuple    =  0.1 * sample

     if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE	,		"""set_timesteps"""       ):
      scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
     elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE	,		"""set_timesteps"""       ):
      snake_case__     :					int    =  num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     snake_case__     :					Any    =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     snake_case__     :					Any    =  dummy_past_residuals[:]

     snake_case__     :					Optional[Any]    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		0	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					Optional[Any]    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		1	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     self.assertEqual(output_a.shape	,		sample.shape       )
     self.assertEqual(output_a.shape	,		output_a.shape       )

     snake_case__     :					Tuple    =  scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		0	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample
     snake_case__     :					Dict    =  scheduler.step_plms(__SCREAMING_SNAKE_CASE	,		1	,		__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       ).prev_sample

     self.assertEqual(output_a.shape	,		sample.shape       )
     self.assertEqual(output_a.shape	,		output_a.shape       )

   def 		__UpperCamelCase						(							self       ):
    for timesteps in [1_0_0, 1_0_0_0]:
     self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    for steps_offset in [0, 1]:
     self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  self.scheduler_classes[0]
    snake_case__     :					Optional[Any]    =  self.get_scheduler_config(steps_offset=1       )
    snake_case__     :					Tuple    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )
    scheduler.set_timesteps(1_0       )
    assert torch.equal(
        scheduler.timesteps	,		torch.LongTensor(
            [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]       )	,		)

   def 		__UpperCamelCase						(							self       ):
    for beta_start, beta_end in zip([0.0001, 0.001]	,		[0.002, 0.02]       ):
     self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE	,		beta_end=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    for t in [1, 5, 1_0]:
     self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    for t, num_inference_steps in zip([1, 5, 1_0]	,		[1_0, 5_0, 1_0_0]       ):
     self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
    snake_case__     :					List[str]    =  2_7

    for scheduler_class in self.scheduler_classes:
     snake_case__     :					str    =  self.dummy_sample
     snake_case__     :					str    =  0.1 * sample

     snake_case__     :					int    =  self.get_scheduler_config()
     snake_case__     :					List[Any]    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

     # before power of 3 fix, would error on first step, so we only need to do two
     for i, t in enumerate(scheduler.prk_timesteps[:2]       ):
      snake_case__     :					Union[str, Any]    =  scheduler.step_prk(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ).prev_sample

   def 		__UpperCamelCase						(							self       ):
    with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
     snake_case__     :					Tuple    =  self.scheduler_classes[0]
     snake_case__     :					Optional[int]    =  self.get_scheduler_config()
     snake_case__     :					str    =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

     scheduler.step_plms(self.dummy_sample	,		1	,		self.dummy_sample       ).prev_sample

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  self.full_loop()
    snake_case__     :					Optional[int]    =  torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
    snake_case__     :					int    =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

    assert abs(result_sum.item() - 198.1318       ) < 1e-2
    assert abs(result_mean.item() - 0.2580       ) < 1e-3

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  self.full_loop(prediction_type="""v_prediction"""       )
    snake_case__     :					List[Any]    =  torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
    snake_case__     :					List[str]    =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

    assert abs(result_sum.item() - 67.3986       ) < 1e-2
    assert abs(result_mean.item() - 0.0878       ) < 1e-3

   def 		__UpperCamelCase						(							self       ):
    # We specify different beta, so that the first alpha is 0.99
    snake_case__     :					Optional[int]    =  self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE	,		beta_start=0.01       )
    snake_case__     :					str    =  torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
    snake_case__     :					Dict    =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

    assert abs(result_sum.item() - 230.0399       ) < 1e-2
    assert abs(result_mean.item() - 0.2995       ) < 1e-3


   def 		__UpperCamelCase						(							self       ):
    # We specify different beta, so that the first alpha is 0.99
    snake_case__     :					Dict    =  self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE	,		beta_start=0.01       )
    snake_case__     :					List[Any]    =  torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
    snake_case__     :					List[Any]    =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

    assert abs(result_sum.item() - 186.9482       ) < 1e-2
    assert abs(result_mean.item() - 0.2434       ) < 1e-3





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_SCREAMING_SNAKE_CASE 		=      logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(
       default="""NER"""  ,     metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}		)
   __lowerCAmelCase           =				field(default=__lowerCAmelCase  ,     metadata={"""help""": """Set this flag to use fast tokenization."""}		)
   # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
   # or just modify its tokenizer_config.json.
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,     )





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}  ,     )
   __lowerCAmelCase           =				field(
       default=128  ,     metadata={
           """help""": (
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           )
       }  ,     )
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}		)





def 			lowercase(	)		->	Optional[Any]:







 '''simple docstring'''
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCamelCase			  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
 if len(sys.argv       ) == 2 and sys.argv[1].endswith(""".json"""       ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
 else:
  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir       )
     and os.listdir(training_args.output_dir       )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      """ --overwrite_output_dir to overcome."""       )

 UpperCamelCase			  =      import_module("""tasks"""       )
 try:
  UpperCamelCase			  =      getattr(UpperCamelCase_							,					model_args.task_type       )
  UpperCamelCase			  =      token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
      f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,					datefmt="""%m/%d/%Y %H:%M:%S"""							,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN							,					)
 logger.warning(
     """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""							,					training_args.local_rank							,					training_args.device							,					training_args.n_gpu							,					bool(training_args.local_rank != -1       )							,					training_args.fpaa							,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank       ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s"""							,					UpperCamelCase_       )

 # Set seed
 set_seed(training_args.seed       )

 # Prepare CONLL-2003 task
 UpperCamelCase			  =      token_classification_task.get_labels(data_args.labels       )
 UpperCamelCase			  =      dict(enumerate(UpperCamelCase_       )       )
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCamelCase			  =      AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path							,					num_labels=UpperCamelCase_							,					idalabel=UpperCamelCase_							,					labelaid={label: i for i, label in enumerate(UpperCamelCase_       )}							,					cache_dir=model_args.cache_dir							,					)
 UpperCamelCase			  =      AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,					cache_dir=model_args.cache_dir							,					use_fast=model_args.use_fast							,					)
 UpperCamelCase			  =      AutoModelForTokenClassification.from_pretrained(
     model_args.model_name_or_path							,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path       )							,					config=UpperCamelCase_							,					cache_dir=model_args.cache_dir							,					)

 # Get datasets
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.train							,					)
     if training_args.do_train
     else None
 )
 UpperCamelCase			  =      (
     TokenClassificationDataset(
         token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.dev							,					)
     if training_args.do_eval
     else None
 )

 def align_predictions(UpperCamelCase_							,					UpperCamelCase_       ) -> Tuple[List[int], List[int]]:
  UpperCamelCase			  =      np.argmax(UpperCamelCase_							,					axis=2       )

  UpperCamelCase  ,     UpperCamelCase			  =      preds.shape

  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]
  UpperCamelCase			  =      [[] for _ in range(UpperCamelCase_       )]

  for i in range(UpperCamelCase_       ):
   for j in range(UpperCamelCase_       ):
    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
     out_label_list[i].append(label_map[label_ids[i][j]]       )
     preds_list[i].append(label_map[preds[i][j]]       )

  return preds_list, out_label_list

 def compute_metrics(UpperCamelCase_       ) -> Dict:
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(p.predictions							,					p.label_ids       )
  return {
      "accuracy_score": accuracy_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "precision": precision_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "recall": recall_score(UpperCamelCase_							,					UpperCamelCase_       ),
      "f1": fa_score(UpperCamelCase_							,					UpperCamelCase_       ),
  }

 # Data collator
 UpperCamelCase			  =      DataCollatorWithPadding(UpperCamelCase_							,					pad_to_multiple_of=8       ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCamelCase			  =      Trainer(
     model=UpperCamelCase_							,					args=UpperCamelCase_							,					train_dataset=UpperCamelCase_							,					eval_dataset=UpperCamelCase_							,					compute_metrics=UpperCamelCase_							,					data_collator=UpperCamelCase_							,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_process_zero():
   tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
 UpperCamelCase			  =      {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""       )

  UpperCamelCase			  =      trainer.evaluate()

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""eval_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    logger.info("""***** Eval results *****"""       )
    for key, value in result.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

   results.update(UpperCamelCase_       )

    # Predict
 if training_args.do_predict:
  UpperCamelCase			  =      TokenClassificationDataset(
      token_classification_task=UpperCamelCase_							,					data_dir=data_args.data_dir							,					tokenizer=UpperCamelCase_							,					labels=UpperCamelCase_							,					model_type=config.model_type							,					max_seq_length=data_args.max_seq_length							,					overwrite_cache=data_args.overwrite_cache							,					mode=Split.test							,					)

  UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      trainer.predict(UpperCamelCase_       )
  UpperCamelCase  ,     UpperCamelCase			  =      align_predictions(UpperCamelCase_							,					UpperCamelCase_       )

  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_results.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    for key, value in metrics.items():
     logger.info("""  %s = %s"""							,					UpperCamelCase_							,					UpperCamelCase_       )
     writer.write("""%s = %s\n""" % (key, value)       )

        # Save predictions
  UpperCamelCase			  =      os.path.join(training_args.output_dir							,					"""test_predictions.txt"""       )
  if trainer.is_world_process_zero():
   with open(UpperCamelCase_							,					"""w"""       ) as writer:
    with open(os.path.join(data_args.data_dir							,					"""test.txt"""       )							,					"""r"""       ) as f:
     token_classification_task.write_predictions_to_file(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

 return results





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()
